Recently, some methods were developed to directly map images into labels with a classification model, by maximizing the mutual information between the labels of the original images and their augmentations [1]}, [2]}, [3]}, or maximizing the likelihood of the cluster assignments between a sample and its nearest neighbors
 [3]}, [5]}, [6]}, [7]}, [8]}. Some of them further generate pseudo-labels to refine the model [7]}, [8]}. Furthermore, some methods were proposed to act as add-on modules in order to revise the classification model via label cleansing and retraining with the refined labels [11]}, [12]}.
